Self-Organization of Sensor Networks with Heterogeneous Connectivity

Abstract

Most research on wireless sensor networks has focused on homogeneous networks where all nodes have identical transmission ranges. However, heterogeneous networks, where nodes have different transmission ranges, are potentially much more efficient. In this chapter, we study how heterogeneous networks can be configured by distributed self-organization algorithms where each node selects its own transmission range based on local information. We define a specific performance function, and show empirically that self-organization based on local information produces networks that are close to optimal, and that including more information provides only marginal benefit. We also investigate whether the quality of networks configured by self-organization results from their generic connectivity distribution (as is argued for scale-free networks) or from their specific pattern of heterogeneous connectivity, finding the latter to be the case. The study confirms that heterogeneous networks outperform homogeneous ones, though with randomly deployed nodes, networks that seek homogeneous out-degree have an advantage over networks that simply use the same transmission range for all nodes. Finally, our simulation results show that highly optimized network configurations are as robust as non-optimized ones with respect to random node failure, but are much more susceptible to targeted attacks that preferentially remove nodes with the highest connectivity, confirming the trade-off between optimality and robustness postulated for optimized complex systems.

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

“…When using self-organisation to engineer decentralised collective systems, differences between system components can also be an important factor in enabling the collective to obtain high performance [Prasath et al 2009;Campbell et al 2011]. Heterogeneity in sensor networks may take on various forms.…”

“…In engineering such systems, the challenge is to find self-organisation algorithms which give rise to optimal forms of such heterogeneity, which in turn lead to high performance at the global level. Prasath et al [2009] highlight two key issues:…”

“…This larger configuration space provides more possibilities, some of which may enable a collective system to better achieve its goal. The heterogeneity considered by Prasath et al [2009] is in terms of the outdegree and wireless communication radius of nodes. They permit only two possibilities for each node's configuration, and compare the effect of using three different cooperative algorithms for determining node types, benchmarking the outcomes against ideal best possible outcomes.…”

“…We have refined Prasath et al's [2009] key issues for engineering heterogeneity in selforganising systems, to fit the context of our smart camera network case study. Our research questions are therefore as follows:…”

The Future of Camera Networks

“…Focussing on sensor networks, Yarvis et al [53] explore the impact of heterogeneity at the network level, and Römer et al [43] observe that, while wireless sensor networks were initially conceived as homogeneous networks of near-identical sensors, in practice many applications contain platform heterogeneity. Further, Prasath et al [37] recommend heterogeneity in wireless sensor networks to generate near-optimal configurations. This raises a further challenge: while heterogeneity can improve global efficiency, how should "good" behaviour for each individual camera be determined?…”